Shameela Shaikh, Ms. Rubeena Afsar 02 09 The maintenance of structural integrity is paramount for ensuring the safety and longevity of critical infrastructure such as bridges Conventional methods for structural crack inspection are often manual laborintensive and susceptible to human error Recent advancements in deep learning and semantic segmentation offer a potential solution to automate this process However a significant obstacle remains the scarcity of highquality annotated datasets required to train robust models This paper presents a novel enhanced deep learning approach for structural crack detection that integrates a powerful semantic segmentation architecture with stateoftheart synthetic data generation The proposed method utilizes the DeepLabV3 model with a ResNet50 backbone to leverage its robust feature extraction and sophisticated multiscale contextual understanding To address the challenge of data scarcity StyleGAN3 is employed to synthesize a large diverse and highly realistic dataset of structural crack images The integration of this synthetic data with the DeepLabV3ResNet50 model is shown to significantly improve segmentation performance and model generalization Experimental results demonstrate that the proposed framework achieves superior accuracy when compared to existing stateoftheart methods This study not only advances the field of automated structural crack analysis but also establishes a new paradigm for using synthetic data to overcome a fundamental bottleneck in deep learning for civil infrastructure applications Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026